{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div align=\"center\"> 置信区间和预测区间 </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
    "from statsmodels.stats.outliers_influence import summary_table\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "    \n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>不良贷款\n",
       "(亿元）</th>\n",
       "      <th>各项贷款余额\n",
       "(亿元)</th>\n",
       "      <th>本年累计应收贷款\n",
       "(亿元)</th>\n",
       "      <th>贷款项目个数\n",
       "(个)</th>\n",
       "      <th>本年固定资产投资额\n",
       "(亿元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.9</td>\n",
       "      <td>67.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5</td>\n",
       "      <td>51.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.1</td>\n",
       "      <td>111.3</td>\n",
       "      <td>19.8</td>\n",
       "      <td>16</td>\n",
       "      <td>90.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.8</td>\n",
       "      <td>173.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>17</td>\n",
       "      <td>73.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   不良贷款\\n(亿元）  各项贷款余额\\n(亿元)  本年累计应收贷款\\n(亿元)  贷款项目个数\\n(个)  本年固定资产投资额\\n(亿元)\n",
       "0         0.9          67.3             6.8            5             51.9\n",
       "1         1.1         111.3            19.8           16             90.9\n",
       "2         4.8         173.0             7.7           17             73.7"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Dataset: 不良贷款 (亿元） ~ 各项贷款余额 \n",
    "\n",
    "db = '/home/lidong/Datasets/'\n",
    "bad_loans_df = pd.read_excel(os.path.join(db, \"Statistics/bad-loans.xls\"), usecols=\"B:F\")\n",
    "bad_loans_df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>各项贷款余额\n",
       "(亿元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>109.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>196.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>102.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    const  各项贷款余额\\n(亿元)\n",
       "22    1.0         109.6\n",
       "23    1.0         196.2\n",
       "24    1.0         102.2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Regress \n",
    "ys_loans_bad = bad_loans_df.iloc[:, 0]\n",
    "xs_loans_balance = bad_loans_df.iloc[:, 1]\n",
    "\n",
    "xs_loans_balance_with_intercept = sm.add_constant(xs_loans_balance)\n",
    "xs_loans_balance_with_intercept[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              不良贷款\n",
      "(亿元）   R-squared:                       0.712\n",
      "Model:                            OLS   Adj. R-squared:                  0.699\n",
      "Method:                 Least Squares   F-statistic:                     56.75\n",
      "Date:                Thu, 20 Dec 2018   Prob (F-statistic):           1.18e-07\n",
      "Time:                        22:12:10   Log-Likelihood:                -51.508\n",
      "No. Observations:                  25   AIC:                             107.0\n",
      "Df Residuals:                      23   BIC:                             109.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const          -0.8295      0.723     -1.147      0.263      -2.325       0.666\n",
      "各项贷款余额\n",
      "(亿元)     0.0379      0.005      7.534      0.000       0.027       0.048\n",
      "==============================================================================\n",
      "Omnibus:                       14.277   Durbin-Watson:                   2.464\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               14.611\n",
      "Skew:                           1.382   Prob(JB):                     0.000672\n",
      "Kurtosis:                       5.527   Cond. No.                         262.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "fitted_result = sm.OLS(endog=ys_loans_bad, exog=xs_loans_balance_with_intercept).fit()\n",
    "print(fitted_result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Obs</th>\n",
       "      <th>Dep Var\n",
       "Population</th>\n",
       "      <th>Predicted\n",
       "Value</th>\n",
       "      <th>Std Error\n",
       "Mean Predict</th>\n",
       "      <th>Mean ci\n",
       "95% low</th>\n",
       "      <th>Mean ci\n",
       "95% upp</th>\n",
       "      <th>Predict ci\n",
       "95% low</th>\n",
       "      <th>Predict ci\n",
       "95% upp</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Std Error\n",
       "Residual</th>\n",
       "      <th>Student\n",
       "Residual</th>\n",
       "      <th>Cook's\n",
       "D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.323739</td>\n",
       "      <td>0.399609</td>\n",
       "      <td>2.497085</td>\n",
       "      <td>4.150393</td>\n",
       "      <td>-0.854683</td>\n",
       "      <td>7.502161</td>\n",
       "      <td>-2.123739</td>\n",
       "      <td>1.939202</td>\n",
       "      <td>-1.095161</td>\n",
       "      <td>0.025465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.605421</td>\n",
       "      <td>0.550175</td>\n",
       "      <td>5.467296</td>\n",
       "      <td>7.743545</td>\n",
       "      <td>2.354400</td>\n",
       "      <td>10.856442</td>\n",
       "      <td>0.594579</td>\n",
       "      <td>1.901972</td>\n",
       "      <td>0.312612</td>\n",
       "      <td>0.004089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.043318</td>\n",
       "      <td>0.406285</td>\n",
       "      <td>2.202853</td>\n",
       "      <td>3.883784</td>\n",
       "      <td>-1.137858</td>\n",
       "      <td>7.224495</td>\n",
       "      <td>0.156682</td>\n",
       "      <td>1.937814</td>\n",
       "      <td>0.080855</td>\n",
       "      <td>0.000144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Obs  Dep Var\\nPopulation  Predicted\\nValue  Std Error\\nMean Predict  \\\n",
       "22  23.0                  1.2          3.323739                 0.399609   \n",
       "23  24.0                  7.2          6.605421                 0.550175   \n",
       "24  25.0                  3.2          3.043318                 0.406285   \n",
       "\n",
       "    Mean ci\\n95% low  Mean ci\\n95% upp  Predict ci\\n95% low  \\\n",
       "22          2.497085          4.150393            -0.854683   \n",
       "23          5.467296          7.743545             2.354400   \n",
       "24          2.202853          3.883784            -1.137858   \n",
       "\n",
       "    Predict ci\\n95% upp  Residual  Std Error\\nResidual  Student\\nResidual  \\\n",
       "22             7.502161 -2.123739             1.939202          -1.095161   \n",
       "23            10.856442  0.594579             1.901972           0.312612   \n",
       "24             7.224495  0.156682             1.937814           0.080855   \n",
       "\n",
       "    Cook's\\nD  \n",
       "22   0.025465  \n",
       "23   0.004089  \n",
       "24   0.000144  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_table, data_nd, ss2_list = summary_table(fitted_result, alpha=0.05)\n",
    "data_df = pd.DataFrame(data_nd, columns=ss2_list)\n",
    "data_df[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Predicted\n",
       "Value</th>\n",
       "      <th>Std Error\n",
       "Mean Predict</th>\n",
       "      <th>Mean ci\n",
       "95% low</th>\n",
       "      <th>Mean ci\n",
       "95% upp</th>\n",
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       "95% upp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.323739</td>\n",
       "      <td>0.399609</td>\n",
       "      <td>2.497085</td>\n",
       "      <td>4.150393</td>\n",
       "      <td>-0.854683</td>\n",
       "      <td>7.502161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.605421</td>\n",
       "      <td>0.550175</td>\n",
       "      <td>5.467296</td>\n",
       "      <td>7.743545</td>\n",
       "      <td>2.354400</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.043318</td>\n",
       "      <td>0.406285</td>\n",
       "      <td>2.202853</td>\n",
       "      <td>3.883784</td>\n",
       "      <td>-1.137858</td>\n",
       "      <td>7.224495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Predicted\\nValue  Std Error\\nMean Predict  Mean ci\\n95% low  \\\n",
       "22          3.323739                 0.399609          2.497085   \n",
       "23          6.605421                 0.550175          5.467296   \n",
       "24          3.043318                 0.406285          2.202853   \n",
       "\n",
       "    Mean ci\\n95% upp  Predict ci\\n95% low  Predict ci\\n95% upp  \n",
       "22          4.150393            -0.854683             7.502161  \n",
       "23          7.743545             2.354400            10.856442  \n",
       "24          3.883784            -1.137858             7.224495  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.iloc[-3:, 2:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Predict ci 95% low</th>\n",
       "      <th>Predict ci 95% upp</th>\n",
       "      <th>Predict std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.854683</td>\n",
       "      <td>7.502161</td>\n",
       "      <td>2.019871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.354400</td>\n",
       "      <td>10.856442</td>\n",
       "      <td>2.054966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-1.137858</td>\n",
       "      <td>7.224495</td>\n",
       "      <td>2.021203</td>\n",
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      ],
      "text/plain": [
       "    Predict ci 95% low  Predict ci 95% upp  Predict std\n",
       "22           -0.854683            7.502161     2.019871\n",
       "23            2.354400           10.856442     2.054966\n",
       "24           -1.137858            7.224495     2.021203"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction interval is the confidence interval for an observation and includes the estimate of the error.\n",
    "prstd, lower95, upper95 = wls_prediction_std(fitted_result, alpha=0.05)\n",
    "pd.DataFrame({'Predict std': prstd, 'Predict ci 95% low':lower95, 'Predict ci 95% upp':upper95})[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_values = data_nd[:, 2]\n",
    "predict_mean_std = data_nd[:, 3]\n",
    "predict_mean_ci_low95, predict_mean_ci_upp95 = data_nd[:, 4:6].T\n",
    "predict_ci_low95, predict_ci_upp95 = data_nd[:, 6:8].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f481fb97c18>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(xs_loans_balance, ys_loans_bad, 'o')\n",
    "plt.plot(xs_loans_balance, predicted_values, '-', lw=2)\n",
    "plt.plot(xs_loans_balance, predict_ci_low95, 'r--', lw=2)\n",
    "plt.plot(xs_loans_balance, predict_ci_upp95, 'r--', lw=2)\n",
    "plt.plot(xs_loans_balance, predict_mean_ci_low95, 'b--', lw=2)\n",
    "plt.plot(xs_loans_balance, predict_mean_ci_upp95, 'b--', lw=2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
